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PubMed2004

Comparison of Bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients.

This study compares a typical heuristic algorithm with classical and Bayesian regression models in ascertaining the presence of acute bronchopulmonary disease events in lung transplant recipients. These models attempt to predict whether an epoch will end in an event, based on the preceding two weeks of data. The data consist of 150 two-week epochs of daily to biweekly spirometry and symptom covariates for 30 subjects over 60 subject-years. Seventy-five 'event' epochs end on a day when an acute bronchopulmonary disease event is documented in the medical record; 75 randomly selected 'non-event' epochs end on a day when no event is documented. The data are partitioned by randomly assigning 15 subjects for training and the remaining 15 subjects for testing. For cross-validation, a second random partition is generated from the same data set. The statistical models are trained and tested on both partitions. For the heuristic algorithm, its historical event classifications on the same test cases are used. Classification performance on both partitions of all models is compared using receiver operating characteristic curves, sensitivity and specificity, and a Shannon information score. Data partition did not appreciably affect statistical model performance. All statistical models, unlike the heuristic algorithm, performed significantly different than chance (family significance < 0.05, Pearson independence chi-square, Bonferroni multiple correction), and better than the heuristic algorithm. The best models were Bayesian changepoint models. Through a clinically oriented discussion, a case classified by all of these algorithms is presented, suggesting the clinical usefulness of the Bayesian approach compared with the classical and heuristic approaches.

Acute Disease↗

PubMed2006

An incremental training method for the probabilistic RBF network.

The probabilistic radial basis function (PRBF) network constitutes a probabilistic version of the RBF network for classification that extends the typical mixture model approach to classification by allowing the sharing of mixture components among all classes. The typical learning method of PRBF for a classification task employs the expectation-maximization (EM) algorithm and depends strongly on the initial parameter values. In this paper, we propose a technique for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components at appropriate positions in the data space. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. After the addition of all components, the algorithm splits every component of the network into subcomponents, each one corresponding to a different class. Experimental results using several well-known classification data sets indicate that the incremental method provides solutions of superior classification performance compared to the hierarchical PRBF training method. We also conducted comparative experiments with the support vector machines method and present the obtained results along with a qualitative comparison of the two approaches.

Algorithms↗

PubMed2002

Mixture of experts classification using a hierarchical mixture model.

A three-level hierarchical mixture model for classification is presented that models the following data generation process: (1) the data are generated by a finite number of sources (clusters), and (2) the generation mechanism of each source assumes the existence of individual internal class-labeled sources (subclusters of the external cluster). The model estimates the posterior probability of class membership similar to a mixture of experts classifier. In order to learn the parameters of the model, we have developed a general training approach based on maximum likelihood that results in two efficient training algorithms. Compared to other classification mixture models, the proposed hierarchical model exhibits several advantages and provides improved classification performance as indicated by the experimental results.

Algorithms↗

PubMedDate not supplied

Adaptive compression of the ambulatory electrocardiogram.

Previous use of the MIT/BIH arrhythmia database, on analog tape, to investigate compression of ambulatory ECG data by average beat subtraction, residual differencing, and Huffman coding of the residuals had shown that with a quantization level of 35 mu V and a sample rate of 100 samples per second, it was possible to store ECG data with average data rates of 174 bits per second (bps), but because of the variation in ECG signals, data rates for different records ranged from 144 bps to 230 bps. In a practical storage system, it is desirable to fix the maximum data rate and store data with a minimum of distortion. For this study the previous compression algorithm was modified to adapt its quantization level to different ECG signal conditions. Two adaptation strategies were investigated. Both adapt the quantization-step size according to the number of bytes required for storing the coded signal, beat arrival times, and beat classifications. The new compression algorithm was tested with data from the MIT/BIH database on CD ROM. With the more successful of the two strategies, the adaptive compression algorithm stored MIT/BIH records with a difference of only 0.8 bps between the record with the highest data rate and the record with the lowest data rate. The average data rate for the entire database was 193.3 bps. Signal-to-compression noise ratios varied from record to record and varied over time for a given record. Average signal to compression noise ratios varied from 26.82 to 532.83.

Algorithms↗

PubMed1987

Algorithmic diagnosis of jaundice.

Extensive clinical and clinical chemical information was collected from 1002 jaundiced patients. By applying Bayes' theorem and logistic discriminant analysis, a diagnostic algorithm was developed based upon 21 of the 107 variables collected. This algorithm permitted a probabilistic classification of jaundiced patients into four diagnostic categories: acute non-obstructive, chronic non-obstructive, benign obstructive and malignant obstructive jaundice. Of the 985 patients with a final diagnosis a correct probabilistic diagnosis (obstruction vs. non-obstruction) was suggested by the algorithm in 867 patients (88%). Adopting a probability limit of 0.80, 683 patients (69%) were correctly classified, 34 patients (3.5%) were wrongly so, and 268 patients (27%) could not be classified with a probability above 0.80 (doubtful cases). The algorithm was also tested in a further series of 110 jaundiced patients and found to perform equally well: 88 patients classified, 22 patients remaining doubtful. Patients with doubtful diagnoses should be referred to a non-invasive test such as ultrasound examination, whereas patients with definite diagnoses can be referred to invasive tests (liver biopsy, direct cholangiography) as appropriate. The diagnostic algorithm seems to be a valuable aid for the preliminary differential diagnosis of the jaundiced patient and can be used in the planning of a diagnostic strategy for the individual patient.

Algorithms↗

PubMed2004

Comparison of proteins based on segments structural similarity.

We present here a simple method for fast and accurate comparison of proteins using their structures. The algorithm is based on structural alignment of segments of Calpha chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity measure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.

Algorithms↗

PubMed2005

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data.

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.

Aged↗

PubMed1988

Analysis of finite mixture of distributions: a statistical tool for biological classification problems.

A microcomputer program to analyze finite mixtures of normal, binomial, Poisson or exponential distributions by a maximum likelihood estimation procedure is described. The program is coded in Turbo Pascal. Some theoretical and practical aspects of the compound distributions are discussed, mainly mathematical characteristics, fitting procedures and tests of hypotheses. The statistical tool, which is a cluster analysis technique, is presented in a general context for applications in biology. In particular an ecological example is briefly described: the age class structure resolution of a white-tailed deer population. To improve the usefulness for classification purposes, the link with discriminant analysis is examined. For a successful analysis of finite mixture of distributions the need for a large sample size is emphasized.

Age Factors↗

PubMed2002

Diagnosis of sleep apnea by automatic analysis of nasal pressure and forced oscillation impedance.

Detecting and differentiating central and obstructive respiratory events is an important aspect of the diagnosis of sleep-related breathing disorders with respect to the choice of an appropriate treatment. The purpose of this study was to evaluate the performance of a new algorithm for automated detection and classification of apneas and hypopneas, compared with visual analysis of standard polysomnographic signals. The algorithm is based on time series analysis of nasal mask pressure and a forced oscillation signal related to mechanical respiratory input impedance, measured at a frequency of 20 Hz throughout the night. The method was applied to all-night measurements on 19 subjects. Two experts in sleep medicine independently scored the corresponding simultaneously recorded polysomnographic signals. Evaluating the agreement between two scorers by a weighted kappa statistic on a second-by-second basis, we found that inter-expert variability and the discrepancy between automatic analysis and visual analysis performed by an expert were not significantly different. Implementation of this algorithm in a device for home monitoring of breathing during sleep might aid in the differential diagnosis of sleep-related breathing disorders and/or as a means for follow-up and treatment control.

Airway Resistance↗

PubMed1994

The development of a decision support system for the pathological diagnosis of human cerebral tumours based on a neural network classifier.

This study describes the use of a topological mapping system in the classification of cerebral tumours and the development of a decision support system based upon that classifier. Fourteen pathological parameters from two hundred primary cerebral tumours are presented as vectors to a topological map. The map, consisting of a grid of neurones, learns the features of each tumour by means of a shortest Euclidean distance algorithm, after which self adaptation of the neurons occurs. An LVQ algorithm performs the final classification. Study of the map reveals that it can correctly classify tumors following their malignancy potential and their cytogenesis. The decision support system uses the network at its core and helps not only in reaching a diagnosis but also in finding the optimal way to reach that diagnosis. The usefulness of such a mapping system lies in the field of education, clinical research and medically acceptable cost reduction.

Algorithms↗

PubMed2003

A practical assessment algorithm for diagnosis of dizziness.

OBJECTIVE: We sought to test a 3-parameter model for diagnosis of dizziness based on the type and temporal characteristics of the dizziness and on hearing status. STUDY DESIGN AND SETTING: We conducted a prospective blinded study at a tertiary referral neurotology practice. Before examination, patients completed a questionnaire reporting type and timing of dizziness symptoms and hearing status. Clinical diagnoses were compared with questionnaire results. RESULTS: Fifty-seven patients completed the questionnaire. We were able to correctly classify 21 (60%) of the 35 subjects who had a common otogenic cause of vertigo by the diagnostic algorithm. CONCLUSION: A simple classification of dizziness by type, timing, and hearing status can be self-reported by patients using a brief questionnaire. This classification scheme is as good as others of much greater complexity. SIGNIFICANCE: The simple classification scheme reported here is based on history alone and facilitates triage of dizzy patients into diagnostic groups for work-up and management.

Adult↗

PubMed1999

Prediction of membrane protein types and subcellular locations.

Membrane proteins are classified according to two different schemes. In scheme 1, they are discriminated among the following five types: (1) type I single-pass transmembrane, (2) type II single-pass transmembrane, (3) multipass transmembrane, (4) lipid chain-anchored membrane, and (5) GPI-anchored membrane proteins. In scheme 2, they are discriminated among the following nine locations: (1) chloroplast, (2) endoplasmic reticulum, (3) Golgi apparatus, (4) lysosome, (5) mitochondria, (6) nucleus, (7) peroxisome, (8) plasma, and (9) vacuole. An algorithm is formulated for predicting the type or location of a given membrane protein based on its amino acid composition. The overall rates of correct prediction thus obtained by both self-consistency and jackknife tests, as well as by an independent dataset test, were around 76-81% for the classification of five types, and 66-70% for the classification of nine cellular locations. Furthermore, classification and prediction were also conducted between inner and outer membrane proteins; the corresponding rates thus obtained were 88-91%. These results imply that the types of membrane proteins, as well as their cellular locations and other attributes, are closely correlated with their amino acid composition. It is anticipated that the classification schemes and prediction algorithm can expedite the functionality determination of new proteins. The concept and method can be also useful in the prioritization of genes and proteins identified by genomics efforts as potential molecular targets for drug design.

Algorithms↗

PubMed1999

A protein taxonomy based on secondary structure.

Does a protein's secondary structure determine its three-dimensional fold? This question is tested directly by analyzing proteins of known structure and constructing a taxonomy based solely on secondary structure. The taxonomy is generated automatically, and it takes the form of a tree in which proteins with similar secondary structure occupy neighboring leaves. Our tree is largely in agreement with results from the structural classification of proteins (SCOP), a multidimensional classification based on homologous sequences, full three-dimensional structure, information about chemistry and evolution, and human judgment. Our findings suggest a simple mechanism of protein evolution.

Algorithms↗

PubMed2003

Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio.

Neural spike sorting is an indispensable step in the analysis of multiunit extracellular neural signal recording. The applicability of spike sorting systems has been limited, mainly to the recording of sufficiently high signal-to-noise ratios, or to the cases where supervised classification can be utilized. We present a novel unsupervised method that shows satisfactory performance even under high background noise. The system consists of an efficient spike detector, a feature extractor that utilizes projection pursuit based on negentropy maximization (Huber, 1985 and Hyvarinen et al, 1999), and an unsupervised classifier based on probability density modeling using mixture of Gaussians (Jain et al., 2000). Our classifier is based on the mixture model with a roughly approximated number of Gaussians and subsequent mode-seeking. It does not require accurate estimation of the number of units present in the recording and, thus, is better suited for use in fully automated systems. The feature extraction stage leads to better performance than those utilizing principal component analysis and two nonlinear mappings for the recordings from the somatosensory cortex of rat and the abdominal ganglion of Aplysia. The classification method yielded correct classification ratio as high as 95%, for data where it was only 66% when a kappa-means-type algorithm was used for the classification stage.

Action Potentials↗

PubMed2004

Fast k-nearest neighbor classification using cluster-based trees.

Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.

Algorithms↗

PubMedDate not supplied

Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines.

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.

Algorithms↗

PubMed2006

Pruning and model-selecting algorithms in the RBF frameworks constructed by support vector learning.

This paper presents the pruning and model-selecting algorithms to the support vector learning for sample classification and function regression. When constructing RBF network by support vector learning we occasionally obtain redundant support vectors which do not significantly affect the final classification and function approximation results. The pruning algorithms primarily based on the sensitivity measure and the penalty term. The kernel function parameters and the position of each support vector are updated in order to have minimal increase in error, and this makes the structure of SVM network more flexible. We illustrate this approach with synthetic data simulation and face detection problem in order to demonstrate the pruning effectiveness.

Algorithms↗

PubMed1993

Influence of "outliers" on the association between laboratory data and histopathological findings in liver biopsy.

Discriminant analysis techniques were used to predict the histopathological findings in liver biopsy specimens in asymptomatic patients with slightly to moderately raised routine liver tests. Moderate to severe fibrosis and/or inflammation were treated as indication for biopsy. Two methods were used to classify patients. One was the dichotomous discrimination between "biopsy necessary" or "biopsy not necessary" groups of patients. The other involved combining two discriminant functions trained separately for recognition of fibrosis or inflammation, and then combined to predict the biopsy necessity. Detection of outliers by standard techniques, directly available in the SPSS-X package, was performed before starting discrimination procedures. Both "sharp" assignment rules and continuous scoring rules were applied to the classification problem. The correct classification rate reached over 85% for the algorithms tested. In the majority of cases the classification was found to be "non-doubtful". Elimination of outliers (especially by standardized residuals) improved the global correct classification rate, but only slightly improved assignment to the "biopsy necessary" group. Routine and complementary laboratory findings were found to be the most discriminating; answers to questionnaire and ultrasound examination were less important. Selection of the most diagnostic features based on "clean" data without outliers enabled us to find interesting medical associations, which were previously masked by extremely asymptomatic values outlying from the main body of the "biopsy necessary" group.

Biopsy↗